How to Use the Vultr MCP in LlamaIndex

Q: What kind of documentation can Vultr provide to LlamaIndex?

The server exposes operational metadata through tools like getaccount and listapplications. These structured results are perfect for building a unified, queryable index alongside your company documents.

Index all Vultr infrastructure data into a searchable knowledge base with LlamaIndex.

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Connect Vultr MCP to LlamaIndex

Create your Vinkius account to connect Vultr to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Vultr with LlamaIndex

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Searchable Bare Metal Inventory

Instead of just listing resources, you index the output of `list_bare_metals` and store it. Later, your application can query this knowledge base: 'Which bare metal instance is running on IP 192.0.2.1?' It uses actual data from the server, not guesses.

Querying Backup History

Running `list_backups` gives you a list of backups, but indexing that output lets you build an RAG application. You can query past operational states and configurations, getting answers grounded in the specific data provided by the server.

Account Configuration Lookups

You can index outputs from `get_account` or even `get_bare_metal_ipv6`. This means you don't just get a current value; you build a knowledge base that answers questions like, 'What were the BGP details for this account last month?'

Setup guide

Set up Vultr MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Vultr MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Vultr tools.",
)
response = await agent.run("List recent Vultr data")

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Common questions about Vultr MCP in LlamaIndex

LlamaIndex takes the raw output from the MCP Server tools (like `list_api_keys`) and turns it into indexed documents. This lets you query past states or configurations using semantic search against your live API data.

The server exposes operational metadata through tools like `get_account` and `list_applications`. These structured results are perfect for building a unified, queryable index alongside your company documents.

The biggest gain is eliminating hallucination. Instead of relying on general knowledge, you get answers directly grounded in API data—for instance, confirming an IP address using `get_bare_metal_ipv4`.

Yes. You can index the results of changing configurations, such as running `set_bare_metal_ipv6_reverse`. Your knowledge base then records that change and its implications.

The server manages various sensitive pieces of information, most notably the details contained within API keys. When you run `list_api_keys`, that output is what gets indexed into your searchable knowledge base.

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